Digital image compression aims to identify and remove redundancies from digital image data in order to be able to store or transmit the data in an efficient form. When compressing natural images, such as photos, a minor loss of fidelity is often accepted in order to achieve a substantial reduction in bit rate, particularly when the infidelity is not perceivable to the human eye. This so-called ‘lossy’ image compression may be achieved by reducing the color space to the most common colors in the image, for example. It may also be achieved by chroma subsampling, taking advantage of the fact that the eye perceives brightness more sharply than color, by dropping half or more of the chrominance information in the image.
As a further example, the well-known JPEG standard uses transform coding, wherein the image data is transformed by the discrete cosine transform (DCT), followed by quantization and entropy coding. In the field of image compression, the JPEG standard provides very efficient compression methods.
In particular, JPEG is the most successful lossy image compression method. However, JPEG compression is prone to blocking artifacts in low-contrast image patches and ringing artifacts in high-contrast image patches. The reason for the blocking artifacts in low-contrast image patches is the sensitivity of the human visual system to noise in flat image patches and the reason for the ringing artifacts is the fact that, applying the same transform to all different kinds of image patches, JPEG tends to neglect high-frequency components in the vicinity of edges.
A possible way of dealing with this situation is, instead of applying the same coding transform throughout the entire image, to use different coding transforms for different kinds of image patches.
In this respect, J. Fu et al. (“Diagonal Discrete Cosine Transforms for Image Coding”, Zuang et al (Eds.): PCM 2006, LNCS 4261, pp. 150-158, Springer-Verlag Berlin Heidelberg 2006) discuss a method for image coding which combines conventional DCTs and diagonal discrete cosine transforms in different modes. However, a way of effectively selecting the most suitable mode for each image patch is not disclosed; instead, a brute-force approach is adopted: quantization and coding is run for all modes and only after all these steps, the best result is chosen according to a rate-distortion criterion. This approach is computationally expensive and may therefore not be well suited for all different kinds of practical application.
Moreover, a desirable property of an image compression scheme is scalability, also referred to as progressive coding or embedded bit streams. Scalability is especially useful for previewing images while loading (e.g. in a web browser) or for providing variable quality access to e.g. databases. Several types of scalability may be distinguished:                Quality progressive or layer progressive: The bitstream successively refines the reconstructed image.        Resolution progressive: First encode a lower image resolution; then encode the difference to higher resolutions.        Component progressive: First encode grey; then color.        
In that respect, the method presented by Fu et al. is limited to the known sequential mode of image compression: if it is not known a priori which transform may be used for a given image patch, the rate-distortion criterion cannot be used in progressive or hierarchical mode, as these modes combine the coefficients of different image patches. For sequential mode coding, it is known that the ordering of the basis functions is crucial for good compression performance. However, the article by Fu et al. does not disclose how to find such orderings.
It is therefore an object of the present invention to provide a method for image coding that is not only more effective in terms of compression but also computationally more efficient. A particular object is to provide a sound method for image coding that may adapt to the image content, thereby improving compression in terms of space requirements and/or image quality, while preserving computational efficiency.
It is a further object of the present invention, to provide a method and device for compressing an image with an improved compression ratio that essentially avoids blocking artifacts in low-contrast image patches and ringing artifacts in high-contrast image patches. It is yet another object, to provide an efficient method and device for compressing an image that allows for scalability of the compressed image.